Analysis of Neural Stem Cells from the subventricular zone sequenced by 10X Genomics 3' Chromium protocol
Analysis
Preparations
Loading the neccessary packages
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[31m✖[30m [34mdplyr[30m::[32mlag()[30m masks [34mstats[30m::lag()[39mlibrary("Seurat")Loading required package: cowplot
Attaching package: ‘cowplot’
The following object is masked from ‘package:ggplot2’:
ggsave
Loading required package: Matrix
Attaching package: ‘Matrix’
The following object is masked from ‘package:tidyr’:
expandlibrary("Matrix")
library("stringr")
library("knitr")
library("kableExtra")
library("pheatmap")
library("RColorBrewer")
library("clusterProfiler")Loading required package: DOSE
DOSE v3.4.0 For help: https://guangchuangyu.github.io/DOSE
If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609
clusterProfiler v3.4.4 For help: https://guangchuangyu.github.io/clusterProfiler
If you use clusterProfiler in published research, please cite:
Guangchuang Yu., Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
Attaching package: ‘clusterProfiler’
The following object is masked from ‘package:purrr’:
simplifylibrary("ReactomePA")ReactomePA v1.22.0 For help: https://guangchuangyu.github.io/ReactomePA
If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479save_csv <- TRUESetup vectors needed for the analysis: Genes which are markers for the cell types
markers.biol.validated <- c("Thbs4","Cxcl14","Cd9","Nr2e1","Id2","Ascl1","Egfr","Dcx","Dlx1","Mki67","Sox2","S100b","Cd24a","Ift88","Foxj1","Pdgfrb","Pecam1","Slc1a3","Gfap","Nes","Cxcl10","Rpl32","Hes1","Hes5")
markers.custom.order <- c("Sfrp5","Bmpr1a","Vcam1","Slc1a3","Id2","Hes1","Hes5","Egfr","Ascl1","Mki67","Rpl22","Cd9","Nr2e1","Sox2","S100b")Load a list of cell cycle specific genes (taken from: http://satijalab.org/seurat/cell_cycle_vignette.html)
cc.genes <- readLines(con = "cell_cycle_genes/cell_cycle_vignette_files/regev_lab_cell_cycle_genes.txt")
cc.genes <- str_to_title(cc.genes)
# Separate markers of G2/M phase and markers of S phase
s.genes <- cc.genes[1:43]
g2m.genes <- cc.genes[44:97]Loading the data
datadir <- "count_table/filtered_gene_bc_matrices_mex/mm10/"
data_10X2 <- Read10X(data.dir = file.path( datadir) )Create a Seurat object with the 10X data
seurat_10X2 <- CreateSeuratObject(raw.data = data_10X2, min.cells = 3, min.genes = 1500, project = "young_vs_old_10X2")Add Metadata and check QC values
mito.genes <- grep(pattern = "^mt-", x = rownames(x = seurat_10X2@data), value = TRUE)
percent.mito <- Matrix::colSums(seurat_10X2@raw.data[mito.genes, ])/Matrix::colSums(seurat_10X2@raw.data)
seurat_10X2 <- AddMetaData(object = seurat_10X2, metadata = percent.mito, col.name = "percent.mito")
VlnPlot(object = seurat_10X2, features.plot = c("nGene", "nUMI", "percent.mito"), nCol = 3 )
Annotate the cells with the age of the animals
We can also annotate the age of the mouse from which the cells were prepared (young or old). We know that all cell barcodes ending in ...-1 are beloning to cells from old old mice, the cell barcodes ending in ...-2 are from young mice.
age.cells <- data.frame( age = as.factor(rownames(seurat_10X2@meta.data)) )
rownames(age.cells) <- rownames(seurat_10X2@meta.data)
age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-1$" , replacement = "old")
age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-2$" , replacement = "young")
age.cells$age_num <- age.cells$age
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "old" , replacement = "1")
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "young" , replacement = "2")
age.cells$age_num <- as.numeric(age.cells$age_num)
age.cells$age <- factor(age.cells$age)
seurat_10X2 <- AddMetaData(seurat_10X2, age.cells , "age")Check scatterplots of nUMI, nGene and % of mitochondrial genes
par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")
Filter the cells which have too high mitochondrial content
seurat_10X2 <- FilterCells(object = seurat_10X2, subset.names = c("nGene", "percent.mito"),
low.thresholds = c(1500, -Inf), high.thresholds = c(4500, 0.10))Check scatterplots of nUMI, nGene and % of mitochondrial genes after filtering
par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")
Log Normalize the data
seurat_10X2 <- NormalizeData(object = seurat_10X2, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
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|----|----|----|----|----|----|----|----|----|----|
**************************************************|Scale the data and regress out the influence of nUMI and percent.mito
seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = c("nUMI", "percent.mito" , "nGene"))Regress out difference between S phase and G2/M Phase
seurat_10X2 <- CellCycleScoring(object = seurat_10X2, s.genes = s.genes, g2m.genes = g2m.genes, set.ident = TRUE)Meta data columns
We can see that the columns S.Score, G2M.Score and Phase are now added to the meta data table
head(seurat_10X2@meta.data)seurat_10X2@meta.data$CC.Difference <- seurat_10X2@meta.data$S.Score - seurat_10X2@meta.data$G2M.Score
seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = "CC.Difference", display.progress = FALSE)Dimensional reduction and clustering
Find variable genes
seurat_10X2 <- FindVariableGenes(object = seurat_10X2, mean.function = ExpMean, dispersion.function = LogVMR,
x.low.cutoff = 0.0125, x.high.cutoff = 4, y.cutoff = 0.5)Calculating gene means
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|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Using these parameters we have identified 2236 genes as variable.
Run PCA
Now we use the detected variable genes to peform PCA on the data
seurat_10X2 <- RunPCA(object = seurat_10X2, pc.genes = seurat_10X2@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5 , seed.use = 2 )
seurat_10X2 <- ProjectPCA(object = seurat_10X2 )
seurat_10X2 <- JackStraw(object = seurat_10X2 )Elbowplot PCA
PCElbowPlot(object = seurat_10X2)
PCA Plot
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)
Run t-SNE
Use graph-based clustering to cluster the cells in our dataset
seurat_10X2 <- FindClusters(seurat_10X2, reduction.type = "pca", dims.use = 1:8, save.SNN = T , force.recalc = TRUE)Plot t-SNE plot
TSNEPlot(object = seurat_10X2)
FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("red","forestgreen") , no.legend = FALSE ) 
FeaturePlot(object = seurat_10X2 , features.plot = "nUMI" , cols.use = c("lightgrey","red") , no.legend = FALSE )
FeaturePlot(object = seurat_10X2 , features.plot = "nGene" , cols.use = c("lightgrey","red") , no.legend = FALSE )
Let's look at the
markers.seurat_10X2 <- FindAllMarkers(seurat_10X2, print.bar = FALSE , only.pos = TRUE , return.thresh = 0.05)
markers.seurat_10X2 %>% filter(cluster == 0)markers.seurat_10X2 %>% filter(cluster == 1)markers.seurat_10X2 %>% filter(cluster == 2)markers.seurat_10X2 %>% filter(cluster == 3)markers.seurat_10X2 %>% filter(cluster == 4)markers.seurat_10X2 %>% filter(cluster == 5)markers.seurat_10X2 %>% filter(cluster == 6)markers.seurat_10X2 %>% filter(cluster == 7)markers.seurat_10X2 %>% filter(cluster == 8)markers.seurat_10X2 %>% filter(cluster == 9)markers.seurat_10X2 %>% filter(cluster == 10)Differential gene expression between cluster 6 and 8
markers_cluster_6_vs_8 <- FindMarkers(object = seurat_10X2 , ident.1 = 6 , ident.2 = 8 , only.pos = TRUE , print.bar = FALSE )
markers_cluster_6_vs_8Cluster 6 shows increased expression of Mapk-Pathway related genes, immediate-early genes and transcription factors, like: Jun, Fos, Atf3, Ier2 ...
markers_cluster_8_vs_6 <- FindMarkers(object = seurat_10X2 , ident.1 = 8 , ident.2 = 6 , only.pos = TRUE , print.bar = FALSE )
markers_cluster_8_vs_6tSNE feature plots: marker genes
TSNEPlot(object = seurat_10X2)
## quiescent Markers
FeaturePlot(object = seurat_10X2 , features.plot = "Thbs4" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Id2" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl14" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Stem Cell markers
FeaturePlot(object = seurat_10X2 , features.plot = "Slc1a3" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Nr2e1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Hes1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Hes5" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Nes" ,cols.use = c("lightblue","red") , no.legend = FALSE)
##
FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl10" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Proliferation markers
FeaturePlot(object = seurat_10X2 , features.plot = "Rpl22" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Rpl32" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Cd9" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Astrocyte markers
FeaturePlot(object = seurat_10X2 , features.plot = "S100b" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Gfap" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Active NSC markers
FeaturePlot(object = seurat_10X2 , features.plot = "Mki67" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Ascl1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Egfr" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## TAPs
FeaturePlot(object = seurat_10X2 , features.plot = "Vcam1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Dlx1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Neuroblast marker
FeaturePlot(object = seurat_10X2 , features.plot = "Dcx" ,cols.use = c("lightblue","red") , no.legend = FALSE)
##
FeaturePlot(object = seurat_10X2 , features.plot = "Sfrp5" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Oligodenrocyte marker
FeaturePlot(object = seurat_10X2 , features.plot = "Olig1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Pdgfra" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Sox10" ,cols.use = c("lightblue","red") , no.legend = FALSE)
Check tSNE Plot with markers found by Basak et al. (2018)
# Load the genes as vector
markers <- c( "Agt","Slc6a9","Etnppl","Slc6a1","Sparc",
"Slc1a3","Bcan","Tspan7","Htra1","Cldn10","Ptn","Acsl6","Fgfr3",
"Sparcl1","Atp1a2","Gpr37l1","Gja1","Prnp","Acsl3","Aqp4","Apoe","Gm26917", # markers for quiescent NSCs - Gm26917 = Rn45s
"Cst3","Clu","Slc1a2","Prdx6","Mt1","Aldoc", # shared between quiescent and primed NSCs - slc1a2 instead of scl1a2 (probably typo)
"Thbs4","Ntrk2","Fxyd1","Gstm1","Igfbp5","S100a6","Itm2b","Sfrp1","Dkk3","C4b",
"Acot1","Luc7l3","Ckb",
"Cpe","Dbi", # primed NSCs and early active NSCs
"Miat","Lima1","Pabpc1","Ascl1","Rpl12","Mycn","Olig2",
"Pcna","Hsp90aa1","Hnrnpab","Ran","Ppia",
"Eef1a1","Ptma","Rpl41","Npm1", "Rpsa", "Fabp7", "Egfr", # active NSCs
"Mki67","Dlx2","Dlx1","Cdca3","Dlx1as",
"Nrep","Tubb2b","Dcx","Btg1","Nfib",
"Gad1","Ndrg4","Snap25","Syt1","Rbfox3",
"Tmsb10","Stmn2","Cd24a","Dlx6os1","Tubb5","Tubb3","Ccnd2","Hmgn2","H2afz","Sox11","Tuba1b","Tmsb4x","Stmn1","Tpt1","Rpl18a"
) interval <- c( seq( 1, length(markers) , by = round(length(markers)/4) ) , length(markers) )
interval[1] 1 24 47 70 92a) Ascl6 - Clu
TSNEPlot(object = seurat_10X2)
for(i in seq( interval[1] , interval[2] )){
FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
b) Sfrp1 - Ascl1
TSNEPlot(object = seurat_10X2)
for(i in seq( interval[2]+1 , interval[3] )){
FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
c) Npm1 - Dcx
TSNEPlot(object = seurat_10X2)
for(i in seq( interval[3]+1 , interval[4] )){
FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
d) Tubb5 - Rpl18a
TSNEPlot(object = seurat_10X2)
for(i in seq( interval[4]+1 , interval[5] )){
FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
Violin plots: marker genes
## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" )
VlnPlot(object = seurat_10X2 , features.plot = "Id2" )
## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" )
VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" )
VlnPlot(object = seurat_10X2 , features.plot = "Hes1" )
VlnPlot(object = seurat_10X2 , features.plot = "Hes5" )
VlnPlot(object = seurat_10X2 , features.plot = "Nes" )
##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" )
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl14" )
## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" )
VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" )
VlnPlot(object = seurat_10X2 , features.plot = "Cd9" )
## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" )
VlnPlot(object = seurat_10X2 , features.plot = "Gfap" )
## Active NSC markers & TAPS
VlnPlot(object = seurat_10X2 , features.plot = "Mki67" )
VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" )
VlnPlot(object = seurat_10X2 , features.plot = "Egfr" )
VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx2" )
VlnPlot(object = seurat_10X2 , features.plot = "Atp1a2" )
## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" )
##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" )
## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" )
VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" )
VlnPlot(object = seurat_10X2 , features.plot = "Sox10" )
Heatmap marker genes
Set identities from marker gene expression
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(0) ) , ident.use = "qNSC1" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(4) ) , ident.use = "qNSC2" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(6,8) ) , ident.use = "aNSC0" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(3,5) ) , ident.use = "aNSC1" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(1) ) , ident.use = "aNSC2" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(2) ) , ident.use = "TAP" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(7) ) , ident.use = "NB" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(10) ) , ident.use = "OPC" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(9) ) , ident.use = "OD" )Build Cluster Tree
seurat_10X2 <- BuildClusterTree(object = seurat_10X2 , pcs.use = 1:8)[1] "Finished averaging RNA for cluster aNSC0"
[1] "Finished averaging RNA for cluster aNSC1"
[1] "Finished averaging RNA for cluster aNSC2"
[1] "Finished averaging RNA for cluster NB"
[1] "Finished averaging RNA for cluster OD"
[1] "Finished averaging RNA for cluster OPC"
[1] "Finished averaging RNA for cluster qNSC1"
[1] "Finished averaging RNA for cluster qNSC2"
[1] "Finished averaging RNA for cluster TAP"
PCA Plot with identities
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)
tSNE feature plots: marker genes
TSNEPlot(object = seurat_10X2)
TSNEPlot(object = seurat_10X2 , do.label = TRUE)
Violin plots: marker genes
celltypes_order <- c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2","TAP","NB","OPC","OD")
## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Id2" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Hes1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Hes5" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Nes" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Cd9" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Gfap" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Active NSC markers
VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Egfr" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## TAPs
VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Sox10" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("nUMI") , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("nGene") , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("percent.mito") , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
Heatmap marker genes
markers_plots <- c( "Thbs4" , "Id2" , "Slc1a3" , "Nr2e1" , "Hes1" , "Hes5" , "Nes" , "Cxcl10" , "Rpl22" , "Rpl32" , "Cd9" , "S100b" , "Gfap" , "Mki67" , "Ascl1" , "Egfr" , "Vcam1" , "Dlx1" , "Dcx" , "Sfrp5" , "Olig1" , "Pdgfra" , "Sox10" )
DoHeatmap(object = seurat_10X2 , genes.use = markers_plots , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE )
DoHeatmap(object = seurat_10X2 , genes.use = markers.custom.order , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE )
DoHeatmap(object = seurat_10X2 , genes.use = markers , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE )
Save identities to meta.data table
seurat_10X2 <- StashIdent(object = seurat_10X2 , save.name = "celltype") Find marker genes for the subpopulations and save them as csv files
markers.seurat_10X2 <- FindAllMarkers(seurat_10X2)
|+ | 1 % ~26s
|++ | 2 % ~27s
|++ | 3 % ~27s
|+++ | 4 % ~27s
|+++ | 5 % ~26s
|++++ | 7 % ~26s
|++++ | 8 % ~26s
|+++++ | 9 % ~25s
|+++++ | 10% ~25s
|++++++ | 11% ~24s
|+++++++ | 12% ~24s
|+++++++ | 13% ~23s
|++++++++ | 14% ~23s
|++++++++ | 15% ~23s
|+++++++++ | 16% ~22s
|+++++++++ | 18% ~22s
|++++++++++ | 19% ~22s
|++++++++++ | 20% ~21s
|+++++++++++ | 21% ~21s
|+++++++++++ | 22% ~21s
|++++++++++++ | 23% ~20s
|+++++++++++++ | 24% ~20s
|+++++++++++++ | 25% ~20s
|++++++++++++++ | 26% ~19s
|++++++++++++++ | 27% ~19s
|+++++++++++++++ | 29% ~19s
|+++++++++++++++ | 30% ~18s
|++++++++++++++++ | 31% ~18s
|++++++++++++++++ | 32% ~18s
|+++++++++++++++++ | 33% ~18s
|++++++++++++++++++ | 34% ~17s
|++++++++++++++++++ | 35% ~17s
|+++++++++++++++++++ | 36% ~17s
|+++++++++++++++++++ | 37% ~17s
|++++++++++++++++++++ | 38% ~16s
|++++++++++++++++++++ | 40% ~16s
|+++++++++++++++++++++ | 41% ~16s
|+++++++++++++++++++++ | 42% ~15s
|++++++++++++++++++++++ | 43% ~15s
|++++++++++++++++++++++ | 44% ~15s
|+++++++++++++++++++++++ | 45% ~14s
|++++++++++++++++++++++++ | 46% ~14s
|++++++++++++++++++++++++ | 47% ~14s
|+++++++++++++++++++++++++ | 48% ~14s
|+++++++++++++++++++++++++ | 49% ~13s
|++++++++++++++++++++++++++ | 51% ~13s
|++++++++++++++++++++++++++ | 52% ~13s
|+++++++++++++++++++++++++++ | 53% ~12s
|+++++++++++++++++++++++++++ | 54% ~12s
|++++++++++++++++++++++++++++ | 55% ~12s
|+++++++++++++++++++++++++++++ | 56% ~11s
|+++++++++++++++++++++++++++++ | 57% ~11s
|++++++++++++++++++++++++++++++ | 58% ~11s
|++++++++++++++++++++++++++++++ | 59% ~11s
|+++++++++++++++++++++++++++++++ | 60% ~10s
|+++++++++++++++++++++++++++++++ | 62% ~10s
|++++++++++++++++++++++++++++++++ | 63% ~10s
|++++++++++++++++++++++++++++++++ | 64% ~09s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
|+++++++++++++++++++++++++++++++++ | 66% ~09s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|+++++++++++++++++++++++++++++++++++ | 68% ~08s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|++++++++++++++++++++++++++++++++++++ | 70% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 26s
|+ | 1 % ~40s
|++ | 2 % ~39s
|++ | 3 % ~38s
|+++ | 4 % ~38s
|+++ | 5 % ~38s
|++++ | 6 % ~37s
|++++ | 7 % ~36s
|+++++ | 8 % ~36s
|+++++ | 9 % ~36s
|++++++ | 10% ~35s
|++++++ | 11% ~35s
|+++++++ | 12% ~35s
|+++++++ | 13% ~34s
|++++++++ | 14% ~34s
|++++++++ | 15% ~33s
|+++++++++ | 16% ~33s
|+++++++++ | 17% ~33s
|++++++++++ | 18% ~32s
|++++++++++ | 19% ~32s
|+++++++++++ | 20% ~31s
|+++++++++++ | 21% ~31s
|++++++++++++ | 22% ~31s
|++++++++++++ | 23% ~30s
|+++++++++++++ | 24% ~30s
|+++++++++++++ | 25% ~30s
|++++++++++++++ | 26% ~29s
|++++++++++++++ | 27% ~29s
|+++++++++++++++ | 28% ~28s
|+++++++++++++++ | 29% ~28s
|++++++++++++++++ | 30% ~28s
|++++++++++++++++ | 31% ~27s
|+++++++++++++++++ | 32% ~27s
|+++++++++++++++++ | 33% ~26s
|++++++++++++++++++ | 34% ~26s
|++++++++++++++++++ | 35% ~26s
|+++++++++++++++++++ | 36% ~25s
|+++++++++++++++++++ | 37% ~25s
|++++++++++++++++++++ | 38% ~24s
|++++++++++++++++++++ | 39% ~24s
|+++++++++++++++++++++ | 40% ~24s
|+++++++++++++++++++++ | 41% ~23s
|++++++++++++++++++++++ | 42% ~23s
|++++++++++++++++++++++ | 43% ~22s
|+++++++++++++++++++++++ | 44% ~22s
|+++++++++++++++++++++++ | 45% ~22s
|++++++++++++++++++++++++ | 46% ~21s
|++++++++++++++++++++++++ | 47% ~21s
|+++++++++++++++++++++++++ | 48% ~20s
|+++++++++++++++++++++++++ | 49% ~20s
|++++++++++++++++++++++++++ | 51% ~20s
|++++++++++++++++++++++++++ | 52% ~19s
|+++++++++++++++++++++++++++ | 53% ~19s
|+++++++++++++++++++++++++++ | 54% ~18s
|++++++++++++++++++++++++++++ | 55% ~18s
|++++++++++++++++++++++++++++ | 56% ~18s
|+++++++++++++++++++++++++++++ | 57% ~17s
|+++++++++++++++++++++++++++++ | 58% ~17s
|++++++++++++++++++++++++++++++ | 59% ~16s
|++++++++++++++++++++++++++++++ | 60% ~16s
|+++++++++++++++++++++++++++++++ | 61% ~16s
|+++++++++++++++++++++++++++++++ | 62% ~15s
|++++++++++++++++++++++++++++++++ | 63% ~15s
|++++++++++++++++++++++++++++++++ | 64% ~14s
|+++++++++++++++++++++++++++++++++ | 65% ~14s
|+++++++++++++++++++++++++++++++++ | 66% ~14s
|++++++++++++++++++++++++++++++++++ | 67% ~13s
|++++++++++++++++++++++++++++++++++ | 68% ~13s
|+++++++++++++++++++++++++++++++++++ | 69% ~12s
|+++++++++++++++++++++++++++++++++++ | 70% ~12s
|++++++++++++++++++++++++++++++++++++ | 71% ~12s
|++++++++++++++++++++++++++++++++++++ | 72% ~11s
|+++++++++++++++++++++++++++++++++++++ | 73% ~11s
|+++++++++++++++++++++++++++++++++++++ | 74% ~10s
|++++++++++++++++++++++++++++++++++++++ | 75% ~10s
|++++++++++++++++++++++++++++++++++++++ | 76% ~10s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~09s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~09s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~08s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~08s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~06s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 39s
|+ | 1 % ~60s
|++ | 2 % ~59s
|++ | 3 % ~59s
|+++ | 4 % ~59s
|+++ | 5 % ~58s
|++++ | 6 % ~57s
|++++ | 7 % ~56s
|+++++ | 8 % ~55s
|+++++ | 9 % ~55s
|++++++ | 10% ~54s
|++++++ | 11% ~54s
|+++++++ | 12% ~53s
|+++++++ | 13% ~52s
|++++++++ | 14% ~52s
|++++++++ | 15% ~52s
|+++++++++ | 16% ~51s
|+++++++++ | 18% ~50s
|++++++++++ | 19% ~50s
|++++++++++ | 20% ~49s
|+++++++++++ | 21% ~48s
|+++++++++++ | 22% ~48s
|++++++++++++ | 23% ~47s
|++++++++++++ | 24% ~46s
|+++++++++++++ | 25% ~46s
|+++++++++++++ | 26% ~45s
|++++++++++++++ | 27% ~44s
|++++++++++++++ | 28% ~44s
|+++++++++++++++ | 29% ~43s
|+++++++++++++++ | 30% ~43s
|++++++++++++++++ | 31% ~42s
|++++++++++++++++ | 32% ~41s
|+++++++++++++++++ | 33% ~41s
|++++++++++++++++++ | 34% ~40s
|++++++++++++++++++ | 35% ~39s
|+++++++++++++++++++ | 36% ~39s
|+++++++++++++++++++ | 37% ~38s
|++++++++++++++++++++ | 38% ~37s
|++++++++++++++++++++ | 39% ~37s
|+++++++++++++++++++++ | 40% ~36s
|+++++++++++++++++++++ | 41% ~36s
|++++++++++++++++++++++ | 42% ~35s
|++++++++++++++++++++++ | 43% ~35s
|+++++++++++++++++++++++ | 44% ~34s
|+++++++++++++++++++++++ | 45% ~33s
|++++++++++++++++++++++++ | 46% ~33s
|++++++++++++++++++++++++ | 47% ~32s
|+++++++++++++++++++++++++ | 48% ~32s
|+++++++++++++++++++++++++ | 49% ~31s
|++++++++++++++++++++++++++ | 51% ~30s
|++++++++++++++++++++++++++ | 52% ~30s
|+++++++++++++++++++++++++++ | 53% ~29s
|+++++++++++++++++++++++++++ | 54% ~28s
|++++++++++++++++++++++++++++ | 55% ~28s
|++++++++++++++++++++++++++++ | 56% ~27s
|+++++++++++++++++++++++++++++ | 57% ~27s
|+++++++++++++++++++++++++++++ | 58% ~26s
|++++++++++++++++++++++++++++++ | 59% ~25s
|++++++++++++++++++++++++++++++ | 60% ~25s
|+++++++++++++++++++++++++++++++ | 61% ~24s
|+++++++++++++++++++++++++++++++ | 62% ~23s
|++++++++++++++++++++++++++++++++ | 63% ~23s
|++++++++++++++++++++++++++++++++ | 64% ~22s
|+++++++++++++++++++++++++++++++++ | 65% ~21s
|+++++++++++++++++++++++++++++++++ | 66% ~21s
|++++++++++++++++++++++++++++++++++ | 67% ~20s
|+++++++++++++++++++++++++++++++++++ | 68% ~20s
|+++++++++++++++++++++++++++++++++++ | 69% ~19s
|++++++++++++++++++++++++++++++++++++ | 70% ~18s
|++++++++++++++++++++++++++++++++++++ | 71% ~18s
|+++++++++++++++++++++++++++++++++++++ | 72% ~17s
|+++++++++++++++++++++++++++++++++++++ | 73% ~16s
|++++++++++++++++++++++++++++++++++++++ | 74% ~16s
|++++++++++++++++++++++++++++++++++++++ | 75% ~15s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~13s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~12s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~09s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 01s
|+ | 1 % ~01m 22s
|++ | 2 % ~01m 22s
|++ | 3 % ~01m 20s
|+++ | 4 % ~01m 19s
|+++ | 5 % ~01m 18s
|++++ | 6 % ~01m 18s
|++++ | 7 % ~01m 17s
|+++++ | 8 % ~01m 16s
|+++++ | 9 % ~01m 15s
|++++++ | 10% ~01m 14s
|++++++ | 11% ~01m 14s
|+++++++ | 12% ~01m 13s
|+++++++ | 13% ~01m 13s
|++++++++ | 14% ~01m 12s
|++++++++ | 15% ~01m 12s
|+++++++++ | 16% ~01m 11s
|+++++++++ | 17% ~01m 10s
|++++++++++ | 18% ~01m 09s
|++++++++++ | 19% ~01m 08s
|+++++++++++ | 20% ~01m 08s
|+++++++++++ | 21% ~01m 07s
|++++++++++++ | 22% ~01m 06s
|++++++++++++ | 23% ~01m 05s
|+++++++++++++ | 24% ~01m 04s
|+++++++++++++ | 26% ~01m 03s
|++++++++++++++ | 27% ~01m 02s
|++++++++++++++ | 28% ~01m 02s
|+++++++++++++++ | 29% ~01m 01s
|+++++++++++++++ | 30% ~60s
|++++++++++++++++ | 31% ~59s
|++++++++++++++++ | 32% ~58s
|+++++++++++++++++ | 33% ~57s
|+++++++++++++++++ | 34% ~56s
|++++++++++++++++++ | 35% ~55s
|++++++++++++++++++ | 36% ~55s
|+++++++++++++++++++ | 37% ~54s
|+++++++++++++++++++ | 38% ~53s
|++++++++++++++++++++ | 39% ~52s
|++++++++++++++++++++ | 40% ~51s
|+++++++++++++++++++++ | 41% ~50s
|+++++++++++++++++++++ | 42% ~50s
|++++++++++++++++++++++ | 43% ~49s
|++++++++++++++++++++++ | 44% ~48s
|+++++++++++++++++++++++ | 45% ~47s
|+++++++++++++++++++++++ | 46% ~46s
|++++++++++++++++++++++++ | 47% ~45s
|++++++++++++++++++++++++ | 48% ~44s
|+++++++++++++++++++++++++ | 49% ~43s
|+++++++++++++++++++++++++ | 50% ~43s
|++++++++++++++++++++++++++ | 51% ~42s
|+++++++++++++++++++++++++++ | 52% ~41s
|+++++++++++++++++++++++++++ | 53% ~40s
|++++++++++++++++++++++++++++ | 54% ~39s
|++++++++++++++++++++++++++++ | 55% ~38s
|+++++++++++++++++++++++++++++ | 56% ~37s
|+++++++++++++++++++++++++++++ | 57% ~37s
|++++++++++++++++++++++++++++++ | 58% ~36s
|++++++++++++++++++++++++++++++ | 59% ~35s
|+++++++++++++++++++++++++++++++ | 60% ~34s
|+++++++++++++++++++++++++++++++ | 61% ~33s
|++++++++++++++++++++++++++++++++ | 62% ~32s
|++++++++++++++++++++++++++++++++ | 63% ~31s
|+++++++++++++++++++++++++++++++++ | 64% ~31s
|+++++++++++++++++++++++++++++++++ | 65% ~30s
|++++++++++++++++++++++++++++++++++ | 66% ~29s
|++++++++++++++++++++++++++++++++++ | 67% ~28s
|+++++++++++++++++++++++++++++++++++ | 68% ~27s
|+++++++++++++++++++++++++++++++++++ | 69% ~26s
|++++++++++++++++++++++++++++++++++++ | 70% ~25s
|++++++++++++++++++++++++++++++++++++ | 71% ~25s
|+++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|++++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 76% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 26s
|+ | 1 % ~02m 30s
|++ | 2 % ~02m 29s
|++ | 3 % ~02m 27s
|+++ | 4 % ~02m 27s
|+++ | 5 % ~02m 25s
|++++ | 6 % ~02m 23s
|++++ | 7 % ~02m 21s
|+++++ | 8 % ~02m 20s
|+++++ | 9 % ~02m 18s
|++++++ | 10% ~02m 17s
|++++++ | 11% ~02m 15s
|+++++++ | 12% ~02m 14s
|+++++++ | 13% ~02m 12s
|++++++++ | 14% ~02m 12s
|++++++++ | 15% ~02m 10s
|+++++++++ | 16% ~02m 08s
|+++++++++ | 17% ~02m 07s
|++++++++++ | 18% ~02m 05s
|++++++++++ | 19% ~02m 04s
|+++++++++++ | 20% ~02m 02s
|+++++++++++ | 21% ~02m 01s
|++++++++++++ | 22% ~01m 59s
|++++++++++++ | 23% ~01m 58s
|+++++++++++++ | 24% ~01m 56s
|+++++++++++++ | 25% ~01m 55s
|++++++++++++++ | 26% ~01m 53s
|++++++++++++++ | 27% ~01m 52s
|+++++++++++++++ | 28% ~01m 50s
|+++++++++++++++ | 29% ~01m 49s
|++++++++++++++++ | 30% ~01m 47s
|++++++++++++++++ | 31% ~01m 45s
|+++++++++++++++++ | 32% ~01m 44s
|+++++++++++++++++ | 33% ~01m 43s
|++++++++++++++++++ | 34% ~01m 41s
|++++++++++++++++++ | 35% ~01m 39s
|+++++++++++++++++++ | 36% ~01m 38s
|+++++++++++++++++++ | 37% ~01m 36s
|++++++++++++++++++++ | 38% ~01m 35s
|++++++++++++++++++++ | 39% ~01m 35s
|+++++++++++++++++++++ | 40% ~01m 33s
|+++++++++++++++++++++ | 41% ~01m 32s
|++++++++++++++++++++++ | 42% ~01m 30s
|++++++++++++++++++++++ | 43% ~01m 28s
|+++++++++++++++++++++++ | 44% ~01m 27s
|+++++++++++++++++++++++ | 45% ~01m 25s
|++++++++++++++++++++++++ | 46% ~01m 24s
|++++++++++++++++++++++++ | 47% ~01m 22s
|+++++++++++++++++++++++++ | 48% ~01m 21s
|+++++++++++++++++++++++++ | 49% ~01m 19s
|++++++++++++++++++++++++++ | 51% ~01m 17s
|++++++++++++++++++++++++++ | 52% ~01m 16s
|+++++++++++++++++++++++++++ | 53% ~01m 14s
|+++++++++++++++++++++++++++ | 54% ~01m 13s
|++++++++++++++++++++++++++++ | 55% ~01m 11s
|++++++++++++++++++++++++++++ | 56% ~01m 10s
|+++++++++++++++++++++++++++++ | 57% ~01m 08s
|+++++++++++++++++++++++++++++ | 58% ~01m 06s
|++++++++++++++++++++++++++++++ | 59% ~01m 05s
|++++++++++++++++++++++++++++++ | 60% ~01m 03s
|+++++++++++++++++++++++++++++++ | 61% ~01m 02s
|+++++++++++++++++++++++++++++++ | 62% ~01m 01s
|++++++++++++++++++++++++++++++++ | 63% ~59s
|++++++++++++++++++++++++++++++++ | 64% ~58s
|+++++++++++++++++++++++++++++++++ | 65% ~56s
|+++++++++++++++++++++++++++++++++ | 66% ~54s
|++++++++++++++++++++++++++++++++++ | 67% ~53s
|++++++++++++++++++++++++++++++++++ | 68% ~51s
|+++++++++++++++++++++++++++++++++++ | 69% ~49s
|+++++++++++++++++++++++++++++++++++ | 70% ~48s
|++++++++++++++++++++++++++++++++++++ | 71% ~46s
|++++++++++++++++++++++++++++++++++++ | 72% ~45s
|+++++++++++++++++++++++++++++++++++++ | 73% ~43s
|+++++++++++++++++++++++++++++++++++++ | 74% ~41s
|++++++++++++++++++++++++++++++++++++++ | 75% ~40s
|++++++++++++++++++++++++++++++++++++++ | 76% ~38s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~37s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~35s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~33s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~32s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~30s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~29s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~27s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~25s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~22s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~21s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~19s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 36s
|+ | 1 % ~01m 51s
|++ | 2 % ~01m 51s
|++ | 3 % ~01m 50s
|+++ | 4 % ~01m 52s
|+++ | 5 % ~01m 50s
|++++ | 6 % ~01m 49s
|++++ | 7 % ~01m 47s
|+++++ | 8 % ~01m 46s
|+++++ | 9 % ~01m 45s
|++++++ | 10% ~01m 43s
|++++++ | 11% ~01m 42s
|+++++++ | 12% ~01m 41s
|+++++++ | 13% ~01m 41s
|++++++++ | 14% ~01m 40s
|++++++++ | 15% ~01m 38s
|+++++++++ | 16% ~01m 37s
|+++++++++ | 18% ~01m 36s
|++++++++++ | 19% ~01m 34s
|++++++++++ | 20% ~01m 33s
|+++++++++++ | 21% ~01m 32s
|+++++++++++ | 22% ~01m 31s
|++++++++++++ | 23% ~01m 32s
|++++++++++++ | 24% ~01m 30s
|+++++++++++++ | 25% ~01m 29s
|+++++++++++++ | 26% ~01m 28s
|++++++++++++++ | 27% ~01m 26s
|++++++++++++++ | 28% ~01m 25s
|+++++++++++++++ | 29% ~01m 24s
|+++++++++++++++ | 30% ~01m 22s
|++++++++++++++++ | 31% ~01m 21s
|++++++++++++++++ | 32% ~01m 20s
|+++++++++++++++++ | 33% ~01m 19s
|++++++++++++++++++ | 34% ~01m 18s
|++++++++++++++++++ | 35% ~01m 16s
|+++++++++++++++++++ | 36% ~01m 15s
|+++++++++++++++++++ | 37% ~01m 14s
|++++++++++++++++++++ | 38% ~01m 13s
|++++++++++++++++++++ | 39% ~01m 11s
|+++++++++++++++++++++ | 40% ~01m 10s
|+++++++++++++++++++++ | 41% ~01m 09s
|++++++++++++++++++++++ | 42% ~01m 08s
|++++++++++++++++++++++ | 43% ~01m 07s
|+++++++++++++++++++++++ | 44% ~01m 06s
|+++++++++++++++++++++++ | 45% ~01m 04s
|++++++++++++++++++++++++ | 46% ~01m 03s
|++++++++++++++++++++++++ | 47% ~01m 02s
|+++++++++++++++++++++++++ | 48% ~01m 01s
|+++++++++++++++++++++++++ | 49% ~59s
|++++++++++++++++++++++++++ | 51% ~59s
|++++++++++++++++++++++++++ | 52% ~58s
|+++++++++++++++++++++++++++ | 53% ~57s
|+++++++++++++++++++++++++++ | 54% ~55s
|++++++++++++++++++++++++++++ | 55% ~54s
|++++++++++++++++++++++++++++ | 56% ~53s
|+++++++++++++++++++++++++++++ | 57% ~52s
|+++++++++++++++++++++++++++++ | 58% ~50s
|++++++++++++++++++++++++++++++ | 59% ~49s
|++++++++++++++++++++++++++++++ | 60% ~48s
|+++++++++++++++++++++++++++++++ | 61% ~47s
|+++++++++++++++++++++++++++++++ | 62% ~45s
|++++++++++++++++++++++++++++++++ | 63% ~44s
|++++++++++++++++++++++++++++++++ | 64% ~43s
|+++++++++++++++++++++++++++++++++ | 65% ~42s
|+++++++++++++++++++++++++++++++++ | 66% ~40s
|++++++++++++++++++++++++++++++++++ | 67% ~39s
|+++++++++++++++++++++++++++++++++++ | 68% ~38s
|+++++++++++++++++++++++++++++++++++ | 69% ~37s
|++++++++++++++++++++++++++++++++++++ | 70% ~35s
|++++++++++++++++++++++++++++++++++++ | 71% ~34s
|+++++++++++++++++++++++++++++++++++++ | 72% ~33s
|+++++++++++++++++++++++++++++++++++++ | 73% ~32s
|++++++++++++++++++++++++++++++++++++++ | 74% ~31s
|++++++++++++++++++++++++++++++++++++++ | 75% ~29s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~28s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~27s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~26s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~24s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~23s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~22s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~21s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~20s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 58s
|+ | 1 % ~02m 15s
|+ | 2 % ~02m 14s
|++ | 3 % ~02m 13s
|++ | 4 % ~02m 14s
|+++ | 5 % ~02m 12s
|+++ | 6 % ~02m 10s
|++++ | 7 % ~02m 08s
|++++ | 8 % ~02m 07s
|+++++ | 9 % ~02m 05s
|+++++ | 10% ~02m 04s
|++++++ | 11% ~02m 02s
|++++++ | 12% ~02m 01s
|+++++++ | 13% ~02m 01s
|+++++++ | 14% ~02m 01s
|++++++++ | 15% ~01m 59s
|++++++++ | 16% ~01m 58s
|+++++++++ | 17% ~01m 56s
|+++++++++ | 18% ~01m 55s
|++++++++++ | 19% ~01m 53s
|++++++++++ | 20% ~01m 52s
|+++++++++++ | 21% ~01m 55s
|+++++++++++ | 22% ~01m 53s
|++++++++++++ | 23% ~01m 51s
|++++++++++++ | 24% ~01m 50s
|+++++++++++++ | 25% ~01m 48s
|+++++++++++++ | 26% ~01m 46s
|++++++++++++++ | 27% ~01m 45s
|++++++++++++++ | 28% ~01m 43s
|+++++++++++++++ | 29% ~01m 42s
|+++++++++++++++ | 30% ~01m 40s
|++++++++++++++++ | 31% ~01m 38s
|++++++++++++++++ | 32% ~01m 37s
|+++++++++++++++++ | 33% ~01m 35s
|+++++++++++++++++ | 34% ~01m 34s
|++++++++++++++++++ | 35% ~01m 33s
|++++++++++++++++++ | 36% ~01m 31s
|+++++++++++++++++++ | 37% ~01m 30s
|+++++++++++++++++++ | 38% ~01m 28s
|++++++++++++++++++++ | 39% ~01m 27s
|++++++++++++++++++++ | 40% ~01m 25s
|+++++++++++++++++++++ | 41% ~01m 24s
|+++++++++++++++++++++ | 42% ~01m 22s
|++++++++++++++++++++++ | 43% ~01m 21s
|++++++++++++++++++++++ | 44% ~01m 19s
|+++++++++++++++++++++++ | 45% ~01m 18s
|+++++++++++++++++++++++ | 46% ~01m 16s
|++++++++++++++++++++++++ | 47% ~01m 15s
|++++++++++++++++++++++++ | 48% ~01m 14s
|+++++++++++++++++++++++++ | 49% ~01m 12s
|+++++++++++++++++++++++++ | 50% ~01m 11s
|++++++++++++++++++++++++++ | 51% ~01m 09s
|++++++++++++++++++++++++++ | 52% ~01m 08s
|+++++++++++++++++++++++++++ | 53% ~01m 06s
|+++++++++++++++++++++++++++ | 54% ~01m 05s
|++++++++++++++++++++++++++++ | 55% ~01m 03s
|++++++++++++++++++++++++++++ | 56% ~01m 02s
|+++++++++++++++++++++++++++++ | 57% ~01m 01s
|+++++++++++++++++++++++++++++ | 58% ~59s
|++++++++++++++++++++++++++++++ | 59% ~58s
|++++++++++++++++++++++++++++++ | 60% ~56s
|+++++++++++++++++++++++++++++++ | 61% ~55s
|+++++++++++++++++++++++++++++++ | 62% ~53s
|++++++++++++++++++++++++++++++++ | 63% ~52s
|++++++++++++++++++++++++++++++++ | 64% ~51s
|+++++++++++++++++++++++++++++++++ | 65% ~49s
|+++++++++++++++++++++++++++++++++ | 66% ~48s
|++++++++++++++++++++++++++++++++++ | 67% ~46s
|++++++++++++++++++++++++++++++++++ | 68% ~45s
|+++++++++++++++++++++++++++++++++++ | 69% ~44s
|+++++++++++++++++++++++++++++++++++ | 70% ~42s
|++++++++++++++++++++++++++++++++++++ | 71% ~41s
|++++++++++++++++++++++++++++++++++++ | 72% ~39s
|+++++++++++++++++++++++++++++++++++++ | 73% ~38s
|+++++++++++++++++++++++++++++++++++++ | 74% ~36s
|++++++++++++++++++++++++++++++++++++++ | 75% ~35s
|++++++++++++++++++++++++++++++++++++++ | 76% ~34s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~32s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~31s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~30s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~28s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~27s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~25s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~24s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~22s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~21s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~20s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~18s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 22s
|+ | 1 % ~01m 17s
|++ | 2 % ~01m 16s
|++ | 3 % ~01m 16s
|+++ | 4 % ~01m 15s
|+++ | 5 % ~01m 14s
|++++ | 6 % ~01m 13s
|++++ | 7 % ~01m 12s
|+++++ | 8 % ~01m 12s
|+++++ | 9 % ~01m 11s
|++++++ | 10% ~01m 10s
|++++++ | 11% ~01m 09s
|+++++++ | 12% ~01m 09s
|+++++++ | 13% ~01m 09s
|++++++++ | 14% ~01m 08s
|++++++++ | 15% ~01m 07s
|+++++++++ | 16% ~01m 06s
|+++++++++ | 18% ~01m 05s
|++++++++++ | 19% ~01m 04s
|++++++++++ | 20% ~01m 04s
|+++++++++++ | 21% ~01m 03s
|+++++++++++ | 22% ~01m 02s
|++++++++++++ | 23% ~01m 01s
|++++++++++++ | 24% ~01m 00s
|+++++++++++++ | 25% ~60s
|+++++++++++++ | 26% ~59s
|++++++++++++++ | 27% ~58s
|++++++++++++++ | 28% ~57s
|+++++++++++++++ | 29% ~56s
|+++++++++++++++ | 30% ~55s
|++++++++++++++++ | 31% ~55s
|++++++++++++++++ | 32% ~54s
|+++++++++++++++++ | 33% ~53s
|++++++++++++++++++ | 34% ~52s
|++++++++++++++++++ | 35% ~52s
|+++++++++++++++++++ | 36% ~51s
|+++++++++++++++++++ | 37% ~50s
|++++++++++++++++++++ | 38% ~49s
|++++++++++++++++++++ | 39% ~48s
|+++++++++++++++++++++ | 40% ~47s
|+++++++++++++++++++++ | 41% ~47s
|++++++++++++++++++++++ | 42% ~46s
|++++++++++++++++++++++ | 43% ~45s
|+++++++++++++++++++++++ | 44% ~44s
|+++++++++++++++++++++++ | 45% ~43s
|++++++++++++++++++++++++ | 46% ~42s
|++++++++++++++++++++++++ | 47% ~42s
|+++++++++++++++++++++++++ | 48% ~41s
|+++++++++++++++++++++++++ | 49% ~40s
|++++++++++++++++++++++++++ | 51% ~39s
|++++++++++++++++++++++++++ | 52% ~38s
|+++++++++++++++++++++++++++ | 53% ~38s
|+++++++++++++++++++++++++++ | 54% ~37s
|++++++++++++++++++++++++++++ | 55% ~36s
|++++++++++++++++++++++++++++ | 56% ~35s
|+++++++++++++++++++++++++++++ | 57% ~34s
|+++++++++++++++++++++++++++++ | 58% ~33s
|++++++++++++++++++++++++++++++ | 59% ~33s
|++++++++++++++++++++++++++++++ | 60% ~32s
|+++++++++++++++++++++++++++++++ | 61% ~31s
|+++++++++++++++++++++++++++++++ | 62% ~30s
|++++++++++++++++++++++++++++++++ | 63% ~29s
|++++++++++++++++++++++++++++++++ | 64% ~29s
|+++++++++++++++++++++++++++++++++ | 65% ~28s
|+++++++++++++++++++++++++++++++++ | 66% ~27s
|++++++++++++++++++++++++++++++++++ | 67% ~26s
|+++++++++++++++++++++++++++++++++++ | 68% ~25s
|+++++++++++++++++++++++++++++++++++ | 69% ~25s
|++++++++++++++++++++++++++++++++++++ | 70% ~24s
|++++++++++++++++++++++++++++++++++++ | 71% ~23s
|+++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|++++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 20s
|+ | 1 % ~50s
|++ | 2 % ~49s
|++ | 3 % ~49s
|+++ | 4 % ~49s
|+++ | 5 % ~48s
|++++ | 7 % ~48s
|++++ | 8 % ~47s
|+++++ | 9 % ~46s
|+++++ | 10% ~47s
|++++++ | 11% ~46s
|++++++ | 12% ~45s
|+++++++ | 13% ~45s
|++++++++ | 14% ~44s
|++++++++ | 15% ~44s
|+++++++++ | 16% ~43s
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|++++++++++ | 20% ~41s
|+++++++++++ | 21% ~41s
|+++++++++++ | 22% ~40s
|++++++++++++ | 23% ~39s
|++++++++++++ | 24% ~39s
|+++++++++++++ | 25% ~38s
|++++++++++++++ | 26% ~38s
|++++++++++++++ | 27% ~37s
|+++++++++++++++ | 28% ~37s
|+++++++++++++++ | 29% ~36s
|++++++++++++++++ | 30% ~36s
|++++++++++++++++ | 32% ~35s
|+++++++++++++++++ | 33% ~34s
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|++++++++++++++++++ | 35% ~33s
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|+++++++++++++++++++ | 37% ~32s
|++++++++++++++++++++ | 38% ~32s
|++++++++++++++++++++ | 39% ~31s
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|+++++++++++++++++++++ | 41% ~30s
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|++++++++++++++++++++++ | 43% ~29s
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|+++++++++++++++++++++++ | 46% ~28s
|++++++++++++++++++++++++ | 47% ~27s
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|+++++++++++++++++++++++++ | 49% ~26s
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|++++++++++++++++++++++++++ | 51% ~25s
|+++++++++++++++++++++++++++ | 52% ~25s
|+++++++++++++++++++++++++++ | 53% ~24s
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|++++++++++++++++++++++++++++ | 55% ~23s
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|++++++++++++++++++++++++++++++ | 59% ~21s
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|+++++++++++++++++++++++++++++++ | 61% ~20s
|+++++++++++++++++++++++++++++++ | 62% ~20s
|++++++++++++++++++++++++++++++++ | 63% ~19s
|+++++++++++++++++++++++++++++++++ | 64% ~18s
|+++++++++++++++++++++++++++++++++ | 65% ~18s
|++++++++++++++++++++++++++++++++++ | 66% ~17s
|++++++++++++++++++++++++++++++++++ | 67% ~17s
|+++++++++++++++++++++++++++++++++++ | 68% ~16s
|+++++++++++++++++++++++++++++++++++ | 70% ~16s
|++++++++++++++++++++++++++++++++++++ | 71% ~15s
|++++++++++++++++++++++++++++++++++++ | 72% ~15s
|+++++++++++++++++++++++++++++++++++++ | 73% ~14s
|+++++++++++++++++++++++++++++++++++++ | 74% ~13s
|++++++++++++++++++++++++++++++++++++++ | 75% ~13s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~12s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 52st-SNE plots with cluster identity or with age
Color the tSNE plot by the age of the animals
x <- FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("slateblue","yellowgreen") , no.legend = FALSE , do.return = TRUE )
tsne_ageplot <- ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal()
plotsize = 5
tsne_ageplot
#ggsave(plot = tsne_ageplot , filename = "age_tsne_hires.png" , width = plotsize , height = plotsize )ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal() # + guides(color = "none")
Color the tSNE by the identified celltypes and subpopulations
g <- TSNEPlot(seurat_10X2 , do.return = TRUE)
ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) +
geom_point(size = 1) +
labs( x = "tSNE_1" , y = "tSNE_2" ) +
coord_equal() +
scale_color_manual(values = c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )
ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) +
geom_point(size = 1) +
labs( x = "tSNE_1" , y = "tSNE_2" ) +
coord_equal() +
# guides(color = "none") +
scale_color_manual(values = c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )
Save the analysis results so far as RDS file
# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene_all_cells.RDS")Identify and filter out contaminating ependymal cells and leukocytes
We find that there are two small clusters in the tSNE plot that lie outside the qNSC2 and the aNSC0. Using the interactive visualisation feature we can determine the names of these cells and check for differentially expressed genes from those.
outside_q2 <- c("GCGAGAATCGCTTAGA-1", "GTTAAGCGTGCACTTA-1", "TCATTTGTCGTCCAGG-1",
"AACGTTGCACGCCAGT-2", "CAACTAGAGTCGCCGT-2", "CCACTACTCGGAATCT-2",
"CCGTTCATCCTGCTTG-2", "CGCTGGAGTAGTACCT-2", "TACTTACTCCTAGGGC-2",
"TAGTTGGCACATTAGC-2", "TGGCTGGCACTCAGGC-2", "TGTATTCCATGTTGAC-2"
)
outside_a0 <- c("ATGGGAGAGATCCGAG-1", "TACACGACAAAGTGCG-1", "CCATGTCTCCTAGGGC-2",
"CGTGAGCCACCATGTA-2", "CTCGAAAAGTGCGATG-2", "TGACGGCCATCGATGT-2"
)seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_a0 , ident.use = "outside_a0")
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_q2 , ident.use = "outside_q2")TSNEPlot(object = seurat_10X2 , do.label = TRUE)
First the cells outside aNSC0
outside_a0_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_a0")
|+ | 1 % ~05m 26s
|+ | 2 % ~05m 19s
|++ | 3 % ~05m 15s
|++ | 4 % ~05m 13s
|+++ | 5 % ~05m 08s
|+++ | 6 % ~05m 03s
|++++ | 7 % ~04m 59s
|++++ | 8 % ~04m 56s
|+++++ | 9 % ~04m 53s
|+++++ | 10% ~04m 49s
|++++++ | 11% ~04m 47s
|++++++ | 12% ~04m 45s
|+++++++ | 13% ~04m 41s
|+++++++ | 14% ~04m 38s
|++++++++ | 15% ~04m 35s
|++++++++ | 16% ~04m 31s
|+++++++++ | 17% ~04m 28s
|+++++++++ | 18% ~04m 25s
|++++++++++ | 19% ~04m 22s
|++++++++++ | 20% ~04m 19s
|+++++++++++ | 21% ~04m 16s
|+++++++++++ | 22% ~04m 13s
|++++++++++++ | 23% ~04m 09s
|++++++++++++ | 24% ~04m 06s
|+++++++++++++ | 25% ~04m 03s
|+++++++++++++ | 26% ~03m 60s
|++++++++++++++ | 27% ~03m 56s
|++++++++++++++ | 28% ~03m 53s
|+++++++++++++++ | 29% ~03m 50s
|+++++++++++++++ | 30% ~03m 47s
|++++++++++++++++ | 31% ~03m 44s
|++++++++++++++++ | 32% ~03m 40s
|+++++++++++++++++ | 33% ~03m 37s
|+++++++++++++++++ | 34% ~03m 34s
|++++++++++++++++++ | 35% ~03m 31s
|++++++++++++++++++ | 36% ~03m 27s
|+++++++++++++++++++ | 37% ~03m 24s
|+++++++++++++++++++ | 38% ~03m 21s
|++++++++++++++++++++ | 39% ~03m 17s
|++++++++++++++++++++ | 40% ~03m 14s
|+++++++++++++++++++++ | 41% ~03m 11s
|+++++++++++++++++++++ | 42% ~03m 08s
|++++++++++++++++++++++ | 43% ~03m 05s
|++++++++++++++++++++++ | 44% ~03m 01s
|+++++++++++++++++++++++ | 45% ~02m 58s
|+++++++++++++++++++++++ | 46% ~02m 55s
|++++++++++++++++++++++++ | 47% ~02m 52s
|++++++++++++++++++++++++ | 48% ~02m 49s
|+++++++++++++++++++++++++ | 49% ~02m 46s
|+++++++++++++++++++++++++ | 50% ~02m 43s
|++++++++++++++++++++++++++ | 51% ~02m 39s
|++++++++++++++++++++++++++ | 52% ~02m 36s
|+++++++++++++++++++++++++++ | 53% ~02m 33s
|+++++++++++++++++++++++++++ | 54% ~02m 30s
|++++++++++++++++++++++++++++ | 55% ~02m 27s
|++++++++++++++++++++++++++++ | 56% ~02m 23s
|+++++++++++++++++++++++++++++ | 57% ~02m 20s
|+++++++++++++++++++++++++++++ | 58% ~02m 17s
|++++++++++++++++++++++++++++++ | 59% ~02m 13s
|++++++++++++++++++++++++++++++ | 60% ~02m 10s
|+++++++++++++++++++++++++++++++ | 61% ~02m 07s
|+++++++++++++++++++++++++++++++ | 62% ~02m 04s
|++++++++++++++++++++++++++++++++ | 63% ~02m 00s
|++++++++++++++++++++++++++++++++ | 64% ~01m 57s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 54s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 51s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 47s
|++++++++++++++++++++++++++++++++++ | 68% ~01m 44s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 41s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 38s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 34s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 31s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 28s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 25s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 21s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 15s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 12s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 08s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~59s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~55s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~52s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~49s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~46s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~42s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~39s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~36s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~33s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~29s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~26s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 05m 25soutside_a0_markersChecking the differentially expressed genes we can see many leukocyte marker genes, amongst them CD45.
## Ptprc = CD45 -> Leukocyte Common Antigen (LCA)
VlnPlot(object = seurat_10X2 , features.plot = c("Ptprc","Slc1a3") , x.lab.rot = TRUE )
Next the cells outside qNSC2
outside_q2_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_q2")
|+ | 1 % ~03m 40s
|+ | 2 % ~03m 41s
|++ | 3 % ~03m 37s
|++ | 4 % ~03m 34s
|+++ | 5 % ~03m 31s
|+++ | 6 % ~03m 29s
|++++ | 7 % ~03m 26s
|++++ | 8 % ~03m 24s
|+++++ | 9 % ~03m 22s
|+++++ | 10% ~03m 19s
|++++++ | 11% ~03m 18s
|++++++ | 12% ~03m 15s
|+++++++ | 13% ~03m 13s
|+++++++ | 14% ~03m 11s
|++++++++ | 15% ~03m 09s
|++++++++ | 16% ~03m 07s
|+++++++++ | 17% ~03m 04s
|+++++++++ | 18% ~03m 02s
|++++++++++ | 19% ~03m 01s
|++++++++++ | 20% ~02m 59s
|+++++++++++ | 21% ~02m 56s
|+++++++++++ | 22% ~02m 54s
|++++++++++++ | 23% ~02m 52s
|++++++++++++ | 24% ~02m 49s
|+++++++++++++ | 25% ~02m 47s
|+++++++++++++ | 26% ~02m 45s
|++++++++++++++ | 27% ~02m 43s
|++++++++++++++ | 28% ~02m 41s
|+++++++++++++++ | 29% ~02m 38s
|+++++++++++++++ | 30% ~02m 36s
|++++++++++++++++ | 31% ~02m 34s
|++++++++++++++++ | 32% ~02m 31s
|+++++++++++++++++ | 33% ~02m 29s
|+++++++++++++++++ | 34% ~02m 27s
|++++++++++++++++++ | 35% ~02m 25s
|++++++++++++++++++ | 36% ~02m 23s
|+++++++++++++++++++ | 37% ~02m 20s
|+++++++++++++++++++ | 38% ~02m 18s
|++++++++++++++++++++ | 39% ~02m 16s
|++++++++++++++++++++ | 40% ~02m 14s
|+++++++++++++++++++++ | 41% ~02m 12s
|+++++++++++++++++++++ | 42% ~02m 09s
|++++++++++++++++++++++ | 43% ~02m 07s
|++++++++++++++++++++++ | 44% ~02m 05s
|+++++++++++++++++++++++ | 45% ~02m 03s
|+++++++++++++++++++++++ | 46% ~02m 01s
|++++++++++++++++++++++++ | 47% ~01m 58s
|++++++++++++++++++++++++ | 48% ~01m 56s
|+++++++++++++++++++++++++ | 49% ~01m 54s
|+++++++++++++++++++++++++ | 50% ~01m 52s
|++++++++++++++++++++++++++ | 51% ~01m 50s
|++++++++++++++++++++++++++ | 52% ~01m 48s
|+++++++++++++++++++++++++++ | 53% ~01m 45s
|+++++++++++++++++++++++++++ | 54% ~01m 43s
|++++++++++++++++++++++++++++ | 55% ~01m 41s
|++++++++++++++++++++++++++++ | 56% ~01m 39s
|+++++++++++++++++++++++++++++ | 57% ~01m 37s
|+++++++++++++++++++++++++++++ | 58% ~01m 34s
|++++++++++++++++++++++++++++++ | 59% ~01m 32s
|++++++++++++++++++++++++++++++ | 60% ~01m 30s
|+++++++++++++++++++++++++++++++ | 61% ~01m 28s
|+++++++++++++++++++++++++++++++ | 62% ~01m 25s
|++++++++++++++++++++++++++++++++ | 63% ~01m 23s
|++++++++++++++++++++++++++++++++ | 64% ~01m 21s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 19s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 16s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 15s
|++++++++++++++++++++++++++++++++++ | 68% ~01m 12s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 10s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 08s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 05s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~59s
|++++++++++++++++++++++++++++++++++++++ | 75% ~56s
|++++++++++++++++++++++++++++++++++++++ | 76% ~54s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~52s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~50s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~47s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~45s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~43s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~40s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~38s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~36s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~34s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~31s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~29s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~27s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~25s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 42soutside_q2_markersChecking the differentially expressed genes we can see ependymal cell genes (Genes from https://pdfs.semanticscholar.org/e833/5f8154f714088147d76701ee0052d3a388b5.pdf)
VlnPlot(object = seurat_10X2 , features.plot = c("Iqca","Bbox1","Apoe","Syne1","Meig1","Foxj1","Slc1a3") , x.lab.rot = TRUE )
Next we exclude these contaminating cells and save the new seurat objects as a RDS file.
cells_to_remove <- WhichCells(object = seurat_10X2 , ident = c("outside_q2","outside_a0"))
cells_to_remove [1] "GCGAGAATCGCTTAGA-1" "GTTAAGCGTGCACTTA-1" "TCATTTGTCGTCCAGG-1"
[4] "AACGTTGCACGCCAGT-2" "CAACTAGAGTCGCCGT-2" "CCACTACTCGGAATCT-2"
[7] "CCGTTCATCCTGCTTG-2" "CGCTGGAGTAGTACCT-2" "TACTTACTCCTAGGGC-2"
[10] "TAGTTGGCACATTAGC-2" "TGGCTGGCACTCAGGC-2" "TGTATTCCATGTTGAC-2"
[13] "ATGGGAGAGATCCGAG-1" "TACACGACAAAGTGCG-1" "CCATGTCTCCTAGGGC-2"
[16] "CGTGAGCCACCATGTA-2" "CTCGAAAAGTGCGATG-2" "TGACGGCCATCGATGT-2"After we made sure we know which kind of celltype these cells are, we can get rid of them in the following analysis
all_cells <- WhichCells(object = seurat_10X2 )
cells_to_keep <- all_cells[! all_cells %in% cells_to_remove]
seurat_10X2 <- SubsetData(object = seurat_10X2 , cells.use = cells_to_keep )
TSNEPlot(object = seurat_10X2 , do.label = TRUE)
Save the results as an RDS file
# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene.RDS")SessionInfo
sessionInfo()R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods
[9] base
other attached packages:
[1] org.Mm.eg.db_3.5.0 AnnotationDbi_1.40.0 IRanges_2.12.0
[4] S4Vectors_0.16.0 bindrcpp_0.2 ReactomePA_1.22.0
[7] clusterProfiler_3.4.4 DOSE_3.4.0 RColorBrewer_1.1-2
[10] pheatmap_1.0.10 kableExtra_0.9.0 knitr_1.20
[13] Seurat_2.2.0 Matrix_1.2-14 cowplot_0.7.0
[16] forcats_0.3.0 stringr_1.3.1 dplyr_0.7.4
[19] purrr_0.2.4 readr_1.1.1 tidyr_0.7.2
[22] tibble_1.4.2 ggplot2_2.2.1 tidyverse_1.2.1
[25] Biobase_2.38.0 BiocGenerics_0.24.0
loaded via a namespace (and not attached):
[1] utf8_1.1.4 R.utils_2.6.0 lme4_1.1-18-1
[4] RSQLite_2.1.1 htmlwidgets_1.2 grid_3.4.3
[7] trimcluster_0.1-2 ranger_0.6.0 BiocParallel_1.12.0
[10] Rtsne_0.11 munsell_0.4.3 codetools_0.2-15
[13] ica_1.0-2 colorspace_1.3-2 GOSemSim_2.4.1
[16] rstudioapi_0.7 ROCR_1.0-7 robustbase_0.92-7
[19] dtw_1.20-1 NMF_0.20.6 labeling_0.3
[22] lars_1.2 mnormt_1.5-5 bit64_0.9-7
[25] rprojroot_1.3-2 diptest_0.75-7 R6_2.2.2
[28] doParallel_1.0.10 VGAM_1.0-3 flexmix_2.3-13
[31] bitops_1.0-6 fgsea_1.4.1 assertthat_0.2.0
[34] SDMTools_1.1-221 scales_0.5.0 nnet_7.3-12
[37] gtable_0.2.0 rlang_0.2.2 MatrixModels_0.4-1
[40] scatterplot3d_0.3-41 splines_3.4.3 lazyeval_0.2.0
[43] ModelMetrics_1.1.0 acepack_1.4.1 broom_0.4.3
[46] checkmate_1.8.5 yaml_2.2.0 reshape2_1.4.2
[49] abind_1.4-5 modelr_0.1.1 backports_1.1.2
[52] qvalue_2.10.0 Hmisc_4.1-1 caret_6.0-73
[55] tools_3.4.3 psych_1.7.8 gridBase_0.4-7
[58] gplots_3.0.1 proxy_0.4-22 ggridges_0.5.0
[61] Rcpp_0.12.18 plyr_1.8.4 base64enc_0.1-3
[64] rpart_4.1-12 pbapply_1.3-1 haven_1.1.2
[67] cluster_2.0.6 magrittr_1.5 data.table_1.11.6
[70] DO.db_2.9 openxlsx_4.1.0 reactome.db_1.62.0
[73] mvtnorm_1.0-5 hms_0.4.2 evaluate_0.11
[76] xtable_1.8-2 rio_0.5.10 mclust_5.2.2
[79] readxl_1.1.0 gridExtra_2.2.1 compiler_3.4.3
[82] KernSmooth_2.23-15 crayon_1.3.4 minqa_1.2.4
[85] R.oo_1.22.0 htmltools_0.3.6 segmented_0.5-1.4
[88] Formula_1.2-3 tclust_1.2-3 lubridate_1.7.1
[91] DBI_1.0.0 diffusionMap_1.1-0.1 MASS_7.3-48
[94] fpc_2.1-10 rappdirs_0.3.1 boot_1.3-20
[97] car_3.0-2 cli_1.0.0 R.methodsS3_1.7.1
[100] gdata_2.17.0 bindr_0.1 igraph_1.0.1
[103] pkgconfig_2.0.2 sn_1.5-0 rvcheck_0.1.0
[106] registry_0.3 numDeriv_2016.8-1 foreign_0.8-69
[109] xml2_1.1.1 foreach_1.4.3 rngtools_1.2.4
[112] pkgmaker_0.22 rvest_0.3.2 digest_0.6.12
[115] tsne_0.1-3 graph_1.56.0 rmarkdown_1.10
[118] cellranger_1.1.0 fastmatch_1.1-0 htmlTable_1.12
[121] curl_3.2 kernlab_0.9-25 gtools_3.5.0
[124] modeltools_0.2-22 graphite_1.24.1 nloptr_1.0.4
[127] nlme_3.1-131 jsonlite_1.5 carData_3.0-1
[130] fansi_0.3.0 viridisLite_0.3.0 pillar_1.3.0
[133] lattice_0.20-35 httr_1.3.1 DEoptimR_1.0-8
[136] survival_2.41-3 GO.db_3.5.0 glue_1.3.0
[139] zip_1.0.0 FNN_1.1 prabclus_2.2-6
[142] iterators_1.0.8 bit_1.1-14 class_7.3-14
[145] stringi_1.2.4 mixtools_1.0.4 blob_1.1.1
[148] latticeExtra_0.6-28 caTools_1.17.1 memoise_1.0.0
[151] irlba_2.1.2 ape_5.1 ---
title: "Analysis of Neural Stem Cells from the subventricular zone sequenced by 10X Genomics 3' Chromium protocol"
output: 
  html_notebook: 
    smart: no
    toc: yes
    toc_float: yes
    df_print: paged
---

# Analysis

## Preparations

### Loading the neccessary packages

```{r}
library("tidyverse")
library("Seurat")
library("Matrix")

library("stringr")
library("knitr")
library("kableExtra")

library("pheatmap")
library("RColorBrewer")

library("clusterProfiler")
library("ReactomePA")
```


```{r}
save_csv <- TRUE
```


<style>
  .border-right {
    border-right: 1px solid grey;
  }
</style>


### Setup vectors needed for the analysis: Genes which are markers for the cell types

```{r}
markers.biol.validated <- c("Thbs4","Cxcl14","Cd9","Nr2e1","Id2","Ascl1","Egfr","Dcx","Dlx1","Mki67","Sox2","S100b","Cd24a","Ift88","Foxj1","Pdgfrb","Pecam1","Slc1a3","Gfap","Nes","Cxcl10","Rpl32","Hes1","Hes5")

markers.custom.order <- c("Sfrp5","Bmpr1a","Vcam1","Slc1a3","Id2","Hes1","Hes5","Egfr","Ascl1","Mki67","Rpl22","Cd9","Nr2e1","Sox2","S100b")
```

### Load a list of cell cycle specific genes (taken from: http://satijalab.org/seurat/cell_cycle_vignette.html)

```{r}
cc.genes <- readLines(con = "cell_cycle_genes/cell_cycle_vignette_files/regev_lab_cell_cycle_genes.txt")
cc.genes <- str_to_title(cc.genes)

# Separate markers of G2/M phase and markers of S phase
s.genes <- cc.genes[1:43]
g2m.genes <- cc.genes[44:97]
```

### Loading the data

```{r}
datadir <- "count_table/filtered_gene_bc_matrices_mex/mm10/"

data_10X2 <- Read10X(data.dir = file.path( datadir) )
```

### Create a Seurat object with the 10X data

```{r}
seurat_10X2 <- CreateSeuratObject(raw.data = data_10X2, min.cells = 3, min.genes = 1500, project = "young_vs_old_10X2")
```

### Add Metadata and check QC values

```{r}
mito.genes <- grep(pattern = "^mt-", x = rownames(x = seurat_10X2@data), value = TRUE)

percent.mito <- Matrix::colSums(seurat_10X2@raw.data[mito.genes, ])/Matrix::colSums(seurat_10X2@raw.data)

seurat_10X2 <- AddMetaData(object = seurat_10X2, metadata = percent.mito, col.name = "percent.mito")

VlnPlot(object = seurat_10X2, features.plot = c("nGene", "nUMI", "percent.mito"), nCol = 3 )
```

### Annotate the cells with the age of the animals

We can also annotate the age of the mouse from which the cells were prepared (young or old). We know that all cell barcodes ending in ...-1 are beloning to cells from old old mice, the cell barcodes ending in ...-2 are from young mice.

```{r annotate_age ,cache=TRUE}
age.cells <- data.frame( age = as.factor(rownames(seurat_10X2@meta.data)) ) 
rownames(age.cells) <- rownames(seurat_10X2@meta.data) 

age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-1$" , replacement = "old")
age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-2$" , replacement = "young")

age.cells$age_num <- age.cells$age
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "old" , replacement = "1")
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "young" , replacement = "2")
age.cells$age_num <- as.numeric(age.cells$age_num)

age.cells$age <- factor(age.cells$age)

seurat_10X2 <- AddMetaData(seurat_10X2, age.cells , "age")
```

### Check scatterplots of nUMI, nGene and % of mitochondrial genes

```{r}
par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")
```

### Filter the cells which have too high mitochondrial content

```{r}
seurat_10X2 <- FilterCells(object = seurat_10X2, subset.names = c("nGene", "percent.mito"), 
    low.thresholds = c(1500, -Inf), high.thresholds = c(4500, 0.10))
```

### Check scatterplots of nUMI, nGene and % of mitochondrial genes after filtering

```{r}
par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")
```

### Log Normalize the data

```{r}
seurat_10X2 <- NormalizeData(object = seurat_10X2, normalization.method = "LogNormalize", scale.factor = 10000)
```

### Scale the data and regress out the influence of nUMI and percent.mito

```{r results='hide'}
seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = c("nUMI", "percent.mito" , "nGene"))
```

### Regress out difference between S phase and G2/M Phase

```{r}
seurat_10X2 <- CellCycleScoring(object = seurat_10X2, s.genes = s.genes, g2m.genes = g2m.genes, set.ident = TRUE)
```

#### Meta data columns

We can see that the columns S.Score, G2M.Score and Phase are now added to the meta data table

```{r}
head(seurat_10X2@meta.data)
```

```{r}
seurat_10X2@meta.data$CC.Difference <- seurat_10X2@meta.data$S.Score - seurat_10X2@meta.data$G2M.Score
seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = "CC.Difference", display.progress = FALSE)
```


## Dimensional reduction and clustering

### Find variable genes

```{r}
seurat_10X2 <- FindVariableGenes(object = seurat_10X2, mean.function = ExpMean, dispersion.function = LogVMR, 
    x.low.cutoff = 0.0125, x.high.cutoff = 4, y.cutoff = 0.5)
```

Using these parameters we have identified 2236 genes as variable.


### Run PCA

Now we use the detected variable genes to peform PCA on the data

```{r results='hide'}
seurat_10X2 <- RunPCA(object = seurat_10X2, pc.genes = seurat_10X2@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5  , seed.use = 2 )
seurat_10X2 <- ProjectPCA(object = seurat_10X2 )
seurat_10X2 <- JackStraw(object = seurat_10X2  )
```

### Elbowplot PCA

```{r}
PCElbowPlot(object = seurat_10X2)
```

### PCA Plot

```{r}
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)

```

### Run t-SNE
```{r results='hide', echo=FALSE  }
seurat_10X2 <- RunTSNE(seurat_10X2, dims.use = 1:8, do.fast = T , seed.use = 1)

TSNEPlot(object = seurat_10X2)
```

## Use graph-based clustering to cluster the cells in our dataset

```{r results='hide'}
seurat_10X2 <- FindClusters(seurat_10X2, reduction.type = "pca", dims.use = 1:8, save.SNN = T , force.recalc = TRUE)
```

### Plot t-SNE plot

```{r}
TSNEPlot(object = seurat_10X2)

FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("red","forestgreen") , no.legend = FALSE ) 

FeaturePlot(object = seurat_10X2 , features.plot = "nUMI" , cols.use = c("lightgrey","red") , no.legend = FALSE )

FeaturePlot(object = seurat_10X2 , features.plot = "nGene" , cols.use = c("lightgrey","red") , no.legend = FALSE )

```

Let's look at the 

```{r}
markers.seurat_10X2 <- FindAllMarkers(seurat_10X2, print.bar = FALSE , only.pos = TRUE , return.thresh = 0.05)

markers.seurat_10X2 %>% filter(cluster == 0)

markers.seurat_10X2 %>% filter(cluster == 1)

markers.seurat_10X2 %>% filter(cluster == 2)

markers.seurat_10X2 %>% filter(cluster == 3)

markers.seurat_10X2 %>% filter(cluster == 4)

markers.seurat_10X2 %>% filter(cluster == 5)

markers.seurat_10X2 %>% filter(cluster == 6)

markers.seurat_10X2 %>% filter(cluster == 7)

markers.seurat_10X2 %>% filter(cluster == 8)

markers.seurat_10X2 %>% filter(cluster == 9)

markers.seurat_10X2 %>% filter(cluster == 10)
```

### Differential gene expression between cluster 6 and 8

```{r}
markers_cluster_6_vs_8 <- FindMarkers(object = seurat_10X2 , ident.1 = 6 , ident.2 = 8 , only.pos = TRUE , print.bar = FALSE )

markers_cluster_6_vs_8
```

Cluster 6 shows increased expression of Mapk-Pathway related genes, immediate-early genes and transcription factors, like: Jun, Fos, Atf3, Ier2 ... 

```{r}
markers_cluster_8_vs_6 <- FindMarkers(object = seurat_10X2 , ident.1 = 8  , ident.2 = 6 , only.pos = TRUE , print.bar = FALSE )

markers_cluster_8_vs_6
```


### tSNE feature plots: marker genes

```{r}
TSNEPlot(object = seurat_10X2)
## quiescent Markers
FeaturePlot(object = seurat_10X2 , features.plot = "Thbs4" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Id2" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl14" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Stem Cell markers
FeaturePlot(object = seurat_10X2 , features.plot = "Slc1a3" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Nr2e1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Hes1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Hes5" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Nes" ,cols.use = c("lightblue","red") , no.legend = FALSE)
##
FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl10" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Proliferation markers
FeaturePlot(object = seurat_10X2 , features.plot = "Rpl22" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Rpl32" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Cd9" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Astrocyte markers
FeaturePlot(object = seurat_10X2 , features.plot = "S100b" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Gfap" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Active NSC markers
FeaturePlot(object = seurat_10X2 , features.plot = "Mki67" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Ascl1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Egfr" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## TAPs
FeaturePlot(object = seurat_10X2 , features.plot = "Vcam1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Dlx1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Neuroblast marker
FeaturePlot(object = seurat_10X2 , features.plot = "Dcx" ,cols.use = c("lightblue","red") , no.legend = FALSE)
##
FeaturePlot(object = seurat_10X2 , features.plot = "Sfrp5" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Oligodenrocyte marker
FeaturePlot(object = seurat_10X2 , features.plot = "Olig1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Pdgfra" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Sox10" ,cols.use = c("lightblue","red") , no.legend = FALSE)
```

### Check tSNE Plot with markers found by Basak et al. (2018)

```{r}  
# Load the genes as vector
markers <- c( "Agt","Slc6a9","Etnppl","Slc6a1","Sparc",
                            "Slc1a3","Bcan","Tspan7","Htra1","Cldn10","Ptn","Acsl6","Fgfr3",
                            "Sparcl1","Atp1a2","Gpr37l1","Gja1","Prnp","Acsl3","Aqp4","Apoe","Gm26917",       # markers for quiescent NSCs - Gm26917 = Rn45s
                            "Cst3","Clu","Slc1a2","Prdx6","Mt1","Aldoc",                                    # shared between quiescent and primed NSCs - slc1a2 instead of scl1a2 (probably typo)
                            "Thbs4","Ntrk2","Fxyd1","Gstm1","Igfbp5","S100a6","Itm2b","Sfrp1","Dkk3","C4b",
                            "Acot1","Luc7l3","Ckb",
                            "Cpe","Dbi",                                                                    # primed NSCs and early active NSCs
                            "Miat","Lima1","Pabpc1","Ascl1","Rpl12","Mycn","Olig2",
                            "Pcna","Hsp90aa1","Hnrnpab","Ran","Ppia",
                            "Eef1a1","Ptma","Rpl41","Npm1", "Rpsa", "Fabp7", "Egfr",                        # active NSCs
                            "Mki67","Dlx2","Dlx1","Cdca3","Dlx1as",
                            "Nrep","Tubb2b","Dcx","Btg1","Nfib",
                            "Gad1","Ndrg4","Snap25","Syt1","Rbfox3",
                            "Tmsb10","Stmn2","Cd24a","Dlx6os1","Tubb5","Tubb3","Ccnd2","Hmgn2","H2afz","Sox11","Tuba1b","Tmsb4x","Stmn1","Tpt1","Rpl18a"
                            )  

```


```{r}  
interval <- c( seq( 1, length(markers) , by = round(length(markers)/4) ) , length(markers) )

interval
```

#### a) Ascl6 - Clu

```{r}  
TSNEPlot(object = seurat_10X2)

for(i in seq( interval[1] , interval[2] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
```

#### b) Sfrp1 - Ascl1

```{r}  
TSNEPlot(object = seurat_10X2)

for(i in seq( interval[2]+1 , interval[3] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
```

#### c) Npm1 - Dcx

```{r}  
TSNEPlot(object = seurat_10X2)

for(i in seq( interval[3]+1 , interval[4] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
```

#### d) Tubb5 - Rpl18a

```{r}  
TSNEPlot(object = seurat_10X2)

for(i in seq( interval[4]+1 , interval[5] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
```

### Violin plots: marker genes

```{r}
## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" )
VlnPlot(object = seurat_10X2 , features.plot = "Id2" )
## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" )
VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" )
VlnPlot(object = seurat_10X2 , features.plot = "Hes1" )
VlnPlot(object = seurat_10X2 , features.plot = "Hes5" )
VlnPlot(object = seurat_10X2 , features.plot = "Nes" )
##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" )
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl14" )
## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" )
VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" )
VlnPlot(object = seurat_10X2 , features.plot = "Cd9" )
## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" )
VlnPlot(object = seurat_10X2 , features.plot = "Gfap" )
## Active NSC markers & TAPS
VlnPlot(object = seurat_10X2 , features.plot = "Mki67" )
VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" )
VlnPlot(object = seurat_10X2 , features.plot = "Egfr" )
VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx2" )
VlnPlot(object = seurat_10X2 , features.plot = "Atp1a2" )
## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" )
##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" )
## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" )
VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" )
VlnPlot(object = seurat_10X2 , features.plot = "Sox10" )
```

### Heatmap marker genes

```{r fig.width=16 , fig.height=8}
markers_plots <- c( "Thbs4" , "Id2" , "Slc1a3" , "Nr2e1" , "Hes1" , "Hes5" , "Nes" , "Cxcl10" , "Rpl22" , "Rpl32" , "Cd9" , "S100b" , "Gfap" , "Mki67" , "Ascl1" , "Egfr" , "Vcam1" , "Dlx1" , "Dcx" , "Sfrp5" , "Olig1" , "Pdgfra" , "Sox10" )

DoHeatmap(object = seurat_10X2 , genes.use = markers_plots)
```


## Set identities from marker gene expression

```{r}
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(0) ) , ident.use = "qNSC1"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(4) ) , ident.use = "qNSC2"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(6,8) ) , ident.use = "aNSC0"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(3,5) ) , ident.use = "aNSC1"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(1) ) , ident.use = "aNSC2"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(2) ) , ident.use = "TAP"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(7) ) , ident.use = "NB"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(10) ) , ident.use = "OPC"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(9) ) , ident.use = "OD"  )

```

### Build Cluster Tree

```{r}
seurat_10X2 <- BuildClusterTree(object = seurat_10X2 , pcs.use = 1:8)
```


### PCA Plot with identities

```{r}
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)

```

### tSNE feature plots: marker genes

```{r}

TSNEPlot(object = seurat_10X2)
TSNEPlot(object = seurat_10X2 , do.label = TRUE)
```

### Violin plots: marker genes

```{r fig.width=6, fig.height=4}
celltypes_order <- c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2","TAP","NB","OPC","OD")


## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Id2" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Hes1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Hes5" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Nes" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Cd9" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Gfap" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Active NSC markers
VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Egfr" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## TAPs
VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Sox10" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
```

```{r}
VlnPlot(object = seurat_10X2 , features.plot = c("nUMI")  , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("nGene")  , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("percent.mito")  , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
```


### Heatmap marker genes

```{r fig.height=12}
markers_plots <- c( "Thbs4" , "Id2" , "Slc1a3" , "Nr2e1" , "Hes1" , "Hes5" , "Nes" , "Cxcl10" , "Rpl22" , "Rpl32" , "Cd9" , "S100b" , "Gfap" , "Mki67" , "Ascl1" , "Egfr" , "Vcam1" , "Dlx1" , "Dcx" , "Sfrp5" , "Olig1" , "Pdgfra" , "Sox10" )

DoHeatmap(object = seurat_10X2 , genes.use = markers_plots , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE  )
```

```{r fig.height=12}
DoHeatmap(object = seurat_10X2 , genes.use = markers.custom.order , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE )
```

```{r fig.height=12}
DoHeatmap(object = seurat_10X2 , genes.use = markers , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE  )
```

## Save identities to meta.data table

```{r}
seurat_10X2 <- StashIdent(object = seurat_10X2 , save.name = "celltype") 
```


## Find marker genes for the subpopulations and save them as csv files

```{r}
markers.seurat_10X2 <- FindAllMarkers(seurat_10X2)

# Split the table into one table for each cluster
markers_clusters <- list()

## Save up and down regulated genes per cluster
for( cl in unique(markers.seurat_10X2$cluster) ){
  markers_clusters[[cl]] <- list()
  markers_clusters[[cl]][["up"]] <- filter(markers.seurat_10X2 , cluster == cl & avg_logFC > 0 )
  markers_clusters[[cl]][["down"]] <- filter(markers.seurat_10X2 , cluster == cl & avg_logFC < 0 )
}

for( cl in unique(markers.seurat_10X2$cluster) ){
  for(j in c("up","down"))
  if(save_csv){ write.csv(x = markers_clusters[[cl]][[j]] , file = paste0("celltype_markers/celltype_" , cl , "_", j , "regulated" , "_markers_10X2_young_old.csv" )) }
}
```

## t-SNE plots with cluster identity or with age 

Color the tSNE plot by the age of the animals

```{r}
x <- FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("slateblue","yellowgreen") , no.legend = FALSE , do.return = TRUE )

tsne_ageplot <- ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal()

plotsize = 5

tsne_ageplot
#ggsave(plot = tsne_ageplot , filename = "age_tsne_hires.png" , width = plotsize , height = plotsize )
```

```{r fig.asp=1}
ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal() # + guides(color = "none")
```

Color the tSNE by the identified celltypes and subpopulations

```{r}
g <- TSNEPlot(seurat_10X2 , do.return = TRUE)

ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) + 
  geom_point(size = 1) + 
  labs( x = "tSNE_1" , y = "tSNE_2" ) + 
  coord_equal() + 
  scale_color_manual(values =   c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )
```

```{r fig.asp=1}
ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) + 
  geom_point(size = 1) + 
  labs( x = "tSNE_1" , y = "tSNE_2" ) + 
  coord_equal() + 
  # guides(color = "none") + 
  scale_color_manual(values =   c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )

```


## Save the analysis results so far as RDS file
```{r}
# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene_all_cells.RDS")
```

## Identify and filter out contaminating ependymal cells and leukocytes

We find that there are two small clusters in the tSNE plot that lie outside the qNSC2 and the aNSC0. Using the interactive visualisation feature we can determine the names of these cells and check for differentially expressed genes from those.

```{r}
outside_q2 <- c("GCGAGAATCGCTTAGA-1", "GTTAAGCGTGCACTTA-1", "TCATTTGTCGTCCAGG-1", 
"AACGTTGCACGCCAGT-2", "CAACTAGAGTCGCCGT-2", "CCACTACTCGGAATCT-2", 
"CCGTTCATCCTGCTTG-2", "CGCTGGAGTAGTACCT-2", "TACTTACTCCTAGGGC-2", 
"TAGTTGGCACATTAGC-2", "TGGCTGGCACTCAGGC-2", "TGTATTCCATGTTGAC-2"
)

outside_a0 <- c("ATGGGAGAGATCCGAG-1", "TACACGACAAAGTGCG-1", "CCATGTCTCCTAGGGC-2", 
"CGTGAGCCACCATGTA-2", "CTCGAAAAGTGCGATG-2", "TGACGGCCATCGATGT-2"
)
```

```{r}
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_a0 , ident.use = "outside_a0")
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_q2 , ident.use = "outside_q2")
```

```{r}
TSNEPlot(object = seurat_10X2 , do.label = TRUE)
```

First the cells outside aNSC0

```{r}
outside_a0_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_a0")

outside_a0_markers
```

Checking the differentially expressed genes we can see many leukocyte marker genes, amongst them CD45.

```{r fig.width=15, fig.height=6}
## Ptprc = CD45 -> Leukocyte Common Antigen (LCA)

VlnPlot(object = seurat_10X2 ,  features.plot = c("Ptprc","Slc1a3") , x.lab.rot = TRUE )
```


Next the cells outside qNSC2

```{r}
outside_q2_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_q2")

outside_q2_markers
```

Checking the differentially expressed genes we can see ependymal cell genes (Genes from https://pdfs.semanticscholar.org/e833/5f8154f714088147d76701ee0052d3a388b5.pdf)

```{r fig.height=10, fig.width=20}
VlnPlot(object = seurat_10X2 ,  features.plot = c("Iqca","Bbox1","Apoe","Syne1","Meig1","Foxj1","Slc1a3") , x.lab.rot = TRUE )
```

Next we exclude these contaminating cells and save the new seurat objects as a RDS file.

```{r}
cells_to_remove <- WhichCells(object = seurat_10X2 , ident = c("outside_q2","outside_a0"))

cells_to_remove
```

After we made sure we know which kind of celltype these cells are, we can get rid of them in the following analysis

```{r}
all_cells <- WhichCells(object = seurat_10X2 )

cells_to_keep <- all_cells[! all_cells %in% cells_to_remove]

seurat_10X2 <- SubsetData(object = seurat_10X2 , cells.use = cells_to_keep )

TSNEPlot(object = seurat_10X2 , do.label = TRUE)
```

## Save the results as an RDS file

```{r}
# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene.RDS")
```



SessionInfo

```{r}
sessionInfo()
```
